Research on wind turbine gearbox fault diagnosis with deep transfer learning method

作者：Xin Wang · 发表于：Zenodo (CERN European Organization for Nuclear Research) · 年份：2022 · DOI：10.5281/zenodo.6563138 · 研究领域：Machine Fault Diagnosis Techniques、Engineering Diagnostics and Reliability

As the most complex component in the transmission system, the operating state of the wind turbine gearbox has a tremendous impact on the monitoring of the health status and operation control of the wind turbine equipment. Abnormalities in wind turbines that lead to downtime not only result in a loss of electrical energy, but also a significant increase in maintenance costs. Therefore, with the wind turbine gearbox as the main object of study, the following studies were carried out: For microscopic local conditions in gearbox gear systems, a method for obtaining modal data using finite element simulation analysis of single tooth faults is proposed. Using a combination of deep auto-encoder structures and BP structures for secondary training strategies, a linear and non-linear performance evaluation method is proposed, which takes into account the relationship between performance and efficiency. Hyper-parameter configuration in deep transfer structures is often arbitrary, so a hierarchical transfer network structure hyper-parameter searching method is proposed to address the gearbox planetary system fault classification problem. The algorithm is validated using the classical LeNet-5 reconfiguration transfer application on a modal dataset of the planetary system. Finally, a stability validation and results analysis of the algorithm performance is carried out. A compressed sensing-based sparse signal decomposition method is proposed, and the structure of the transfer network is re...